Chapter 2 Electronic Dance Music & Cognitive Flow

Although this report primarily seeks to identify the musical features that can induce cognitive flow, it is important to acknowledge that the composition of a song is a complex process. Users’ listening experiences are significantly influenced by factors beyond musical features, such as the artists performing the songs and the overall quality of the song. To control for confounding variables that may affect users’ cognitive flow state, and to maximize the mediating role of cognitive flow in leading to eudaimonia and self-transcendence, the genre of a song is utilized as a control variable in this study, especially considered how genres affect users’ preferences and familiarity of music.

Here is a visualization showing the popularity/likability of different music genres by release year.

gg <- ggplot(spotify_data, aes(x = release_year, y = genre_popularity, group = playlist_genre, color = playlist_genre)) +
  geom_line() + geom_point() +
  labs(title = "Genre Popularity Over Years", x = "Year", y = "Genre Popularity") +
  theme_minimal()
p <- ggplotly(gg)
p

Based on this plot, it can be firstly understood that Spotify officially categorizes songs into six genres: electronic dance music, Latin music, pop music, rhythm & blues, rap, and rock music.